| Title: | Identifying Similar T Cell Receptor Hyper-Variable Sequences with 'ClusTCR2' |
|---|---|
| Description: | Enhancing T cell receptor (TCR) sequence analysis, 'ClusTCR2', based on 'ClusTCR' python program, leverages Hamming distance to compare the complement-determining region three (CDR3) sequences for sequence similarity, variable gene (V gene) and length. The second step employs the Markov Cluster Algorithm to identify clusters within an undirected graph, providing a summary of amino acid motifs and matrix for generating network plots. Tailored for single-cell RNA-seq data with integrated TCR-seq information, 'ClusTCR2' is integrated into the Single Cell TCR and Expression Grouped Ontologies (STEGO) R application or 'STEGO.R'. See the two publications for more details. Sebastiaan Valkiers, Max Van Houcke, Kris Laukens, Pieter Meysman (2021) <doi:10.1093/bioinformatics/btab446>, Kerry A. Mullan, My Ha, Sebastiaan Valkiers, Nicky de Vrij, Benson Ogunjimi, Kris Laukens, Pieter Meysman (2023) <doi:10.1101/2023.09.27.559702>. |
| Authors: | Kerry A. Mullan [aut, cre], Sebastiaan Valkiers [aut, ctb], Kris Laukens [aut, ctb], Pieter Meysman [aut, ctb] |
| Maintainer: | Kerry A. Mullan <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.7.3.01 |
| Built: | 2024-12-13 06:35:33 UTC |
| Source: | CRAN |
Creates ClusTCR matrix This function identifies similar CDR3 amino acid sequences based on the same length and V_gene
ClusTCR(my_file, allele = NULL, v_gene = "v_call")ClusTCR(my_file, allele = NULL, v_gene = "v_call")
my_file |
uploaded file with junction_aa (CD3 sequences), variable gene. |
allele |
The allele, if present as *00 will be removed if the user requires it. |
v_gene |
Variable gene column name |
X by Y matrix of structurally related CDR3 sequences.
# Example usage of ClusTCR function with a stored file example_file <- read.csv(system.file("extdata", "my_data.csv", package = "ClusTCR2")) # Perform clustering using ClusTCR function step1 <- ClusTCR(example_file, allele = FALSE) # Print the result print(step1)# Example usage of ClusTCR function with a stored file example_file <- read.csv(system.file("extdata", "my_data.csv", package = "ClusTCR2")) # Perform clustering using ClusTCR function step1 <- ClusTCR(example_file, allele = FALSE) # Print the result print(step1)
Creates ClusTCR matrix This function identifies similar CDR3 amino acid sequences based on the same length and V_gene
ClusTCR_Large(my_file, allele = NULL, v_gene = "v_call")ClusTCR_Large(my_file, allele = NULL, v_gene = "v_call")
my_file |
uploaded file with junction_aa (CD3 sequences), variable gene. |
allele |
The allele, if present as *00 will be removed if the user requires it. |
v_gene |
Variable gene column name |
X by Y matrix of structurally related CDR3 sequences.
Copied code from ggnet's ggnet2 function
ggnet2( net, mode = "fruchtermanreingold", layout.par = NULL, layout.exp = 0, alpha = 1, color = "grey75", shape = 19, size = 9, max_size = 9, na.rm = NA, palette = NULL, alpha.palette = NULL, alpha.legend = NA, color.palette = palette, color.legend = NA, shape.palette = NULL, shape.legend = NA, size.palette = NULL, size.legend = NA, size.zero = FALSE, size.cut = FALSE, size.min = NA, size.max = NA, label = FALSE, label.alpha = 1, label.color = "black", label.size = max_size/2, label.trim = FALSE, node.alpha = alpha, node.color = color, node.label = label, node.shape = shape, node.size = size, edge.alpha = 1, edge.color = "grey50", edge.lty = "solid", edge.size = 0.25, edge.label = NULL, edge.label.alpha = 1, edge.label.color = label.color, edge.label.fill = "white", edge.label.size = max_size/2, arrow.size = 0, arrow.gap = 0, arrow.type = "closed", legend.size = 9, legend.position = "right", ... )ggnet2( net, mode = "fruchtermanreingold", layout.par = NULL, layout.exp = 0, alpha = 1, color = "grey75", shape = 19, size = 9, max_size = 9, na.rm = NA, palette = NULL, alpha.palette = NULL, alpha.legend = NA, color.palette = palette, color.legend = NA, shape.palette = NULL, shape.legend = NA, size.palette = NULL, size.legend = NA, size.zero = FALSE, size.cut = FALSE, size.min = NA, size.max = NA, label = FALSE, label.alpha = 1, label.color = "black", label.size = max_size/2, label.trim = FALSE, node.alpha = alpha, node.color = color, node.label = label, node.shape = shape, node.size = size, edge.alpha = 1, edge.color = "grey50", edge.lty = "solid", edge.size = 0.25, edge.label = NULL, edge.label.alpha = 1, edge.label.color = label.color, edge.label.fill = "white", edge.label.size = max_size/2, arrow.size = 0, arrow.gap = 0, arrow.type = "closed", legend.size = 9, legend.position = "right", ... )
net |
net plot from step 2. |
mode |
= "fruchtermanreingold" |
layout.par |
= NULL, |
layout.exp |
= 0 |
alpha |
= 1 |
color |
= "grey75" |
shape |
= 19 |
size |
= 9 |
max_size |
= 9 |
na.rm |
= NA |
palette |
= NULL |
alpha.palette |
= NULL |
alpha.legend |
= NA |
color.palette |
= palette |
color.legend |
= NA |
shape.palette |
= NULL |
shape.legend |
= NA |
size.palette |
= NULL |
size.legend |
= NA |
size.zero |
= FALSE |
size.cut |
= FALSE |
size.min |
= NA |
size.max |
= NA |
label |
= FALSE |
label.alpha |
= 1 |
label.color |
= "black" |
label.size |
= max_size/2 |
label.trim |
= FALSE |
node.alpha |
see |
node.color |
see |
node.label |
see |
node.shape |
see |
node.size |
see |
edge.alpha |
= 1 |
edge.color |
the color of the edges, as a color value, a vector of color
values, or as an edge attribute containing color values.
Defaults to |
edge.lty |
= "solid" |
edge.size |
= 0.25 |
edge.label |
= NULL |
edge.label.alpha |
= 1 |
edge.label.color |
= label.color |
edge.label.fill |
= "white" |
edge.label.size |
= max_size/2 |
arrow.size |
= 0 |
arrow.gap |
= 0 |
arrow.type |
= "closed" |
legend.size |
= 9 |
legend.position |
= "right" |
... |
Other functions in ggplot2 |
A ggplot object displaying the network plot.
Create the files for labeling the linked clusters from ClusTCR_list_to_matrix function
mcl_cluster(my_file, max.iter = 10, inflation = 1, expansion = 1)mcl_cluster(my_file, max.iter = 10, inflation = 1, expansion = 1)
my_file |
Matrix file produce from ClusTCR |
max.iter |
Number of iterations to find the steady state of MCL. |
inflation |
numeric value |
expansion |
numeric value |
A list containing two elements:
'Cluster_lab': Data frame containing information about the clusters
'Normalised_tabel': Normalized table used in the clustering process
# Example usage of mcl_cluster function with a stored file example_file <- read.csv(system.file("extdata", "my_data.csv",package = "ClusTCR2")) # Perform clustering using mcl_cluster function step1 <- ClusTCR(example_file,allele = FALSE) # perform mcl step2 <- mcl_cluster(step1)# Example usage of mcl_cluster function with a stored file example_file <- read.csv(system.file("extdata", "my_data.csv",package = "ClusTCR2")) # Perform clustering using mcl_cluster function step1 <- ClusTCR(example_file,allele = FALSE) # perform mcl step2 <- mcl_cluster(step1)
Create the files for labeling the linked clusters from ClusTCR_list_to_matrix function
mcl_cluster_large(my_file, max.iter = 10, inflation = 1, expansion = 1)mcl_cluster_large(my_file, max.iter = 10, inflation = 1, expansion = 1)
my_file |
Matrix file produce from ClusTCR |
max.iter |
Number of iterations to find the steady state of MCL. |
inflation |
numeric value |
expansion |
numeric value |
A list containing two elements:
'Cluster_lab': Data frame containing information about the clusters
'Normalised_tabel': Normalized table used in the clustering process
Code for plotting the Motif based on a specific CDR3 length and V gene (see netplot_ClusTCR2 for details).
Motif_from_cluster_file( ClusTCR, Clust_selected = NULL, selected_cluster_column = "Clust_size_order" )Motif_from_cluster_file( ClusTCR, Clust_selected = NULL, selected_cluster_column = "Clust_size_order" )
ClusTCR |
Cluster file produced from mcl_cluster. |
Clust_selected |
Select which cluster to review. |
selected_cluster_column |
Select the column "Clust_size_order" of the cluster ordered. |
A ggplot object representing the motif.
Code for plotting the Motif based on a specific CDR3 length and V gene (see netplot_ClusTCR2 for ).
motif_plot( ClusTCR, Clust_column_name = "Clust_size_order", Clust_selected = NULL )motif_plot( ClusTCR, Clust_column_name = "Clust_size_order", Clust_selected = NULL )
ClusTCR |
Matrix file produce from mcl_cluster |
Clust_column_name |
Name of clustering column from mcl_cluster file e.g. cluster |
Clust_selected |
Select which cluster to display. Only one at a time. |
A ggplot object representing the motif.
# Example usage of mcl_cluster function with a stored file example_file <- read.csv(system.file("extdata", "my_data.csv",package = "ClusTCR2")) # Perform clustering using mcl_cluster function step1 <- ClusTCR(example_file,allele = FALSE) # perform mcl step2 <- mcl_cluster(step1) # print the motif plot for the simple clustering print(motif_plot(step2,Clust_selected = 1))# Example usage of mcl_cluster function with a stored file example_file <- read.csv(system.file("extdata", "my_data.csv",package = "ClusTCR2")) # Perform clustering using mcl_cluster function step1 <- ClusTCR(example_file,allele = FALSE) # perform mcl step2 <- mcl_cluster(step1) # print the motif plot for the simple clustering print(motif_plot(step2,Clust_selected = 1))
Code for plotting the Motif based on a specific CDR3 length and V gene (see netplot_ClusTCR2 for details).
motif_plot_large( ClusTCRFile_large, Clust_column_name = "Clust_size_order", Clust_selected = NULL )motif_plot_large( ClusTCRFile_large, Clust_column_name = "Clust_size_order", Clust_selected = NULL )
ClusTCRFile_large |
Matrix file produced from mcl_cluster_large. |
Clust_column_name |
Name of clustering column from mcl_cluster file e.g. cluster. |
Clust_selected |
Select which cluster to display. Only one at a time. |
A ggplot object representing the motif.
Code for displaying the network.
netplot_ClusTCR2( ClusTCR, filter_plot = 0, Clust_selected = 1, selected_col = "purple", selected_text_col = "black", selected_text_size = 3, non_selected_text_size = 2, Clust_column_name = "cluster", label = c("Name", "cluster", "CDR3", "V_gene", "Len"), non_selected_col = "grey80", non_selected_text_col = "grey40", alpha_selected = 1, alpha_non_selected = 0.5, colour = "color_test", all.colour = "default" )netplot_ClusTCR2( ClusTCR, filter_plot = 0, Clust_selected = 1, selected_col = "purple", selected_text_col = "black", selected_text_size = 3, non_selected_text_size = 2, Clust_column_name = "cluster", label = c("Name", "cluster", "CDR3", "V_gene", "Len"), non_selected_col = "grey80", non_selected_text_col = "grey40", alpha_selected = 1, alpha_non_selected = 0.5, colour = "color_test", all.colour = "default" )
ClusTCR |
File produced from mcl_cluster |
filter_plot |
Filter's plot to remove connects grater than # e.g. 2 = 3 or more connections. |
Clust_selected |
Select which cluster to label. |
selected_col |
Color of selected cluster (Default = purple) |
selected_text_col |
Color of selected cluster text (Default = black) |
selected_text_size |
Text size of selected cluster (Default = 3) |
non_selected_text_size |
Text size of non-selected clusters (Default = 2) |
Clust_column_name |
Name of clustering column from mcl_cluster file e.g. cluster (Re-numbering the original_cluster), Original_cluster, Clust_size_order (Based on cluster size e.g. number of nodes) |
label |
Name to display on cluster: Name (CDR3_V_gene_Cluster), cluster, CDR3, V_gene, Len (length of CDR3 sequence), CDR3_selected, V_gene_selected, Name_selected,cluster_selected, (_selected only prints names of the chosen cluster), None |
non_selected_col |
Color of selected cluster (Default = grey80) |
non_selected_text_col |
Color of selected clusters text (Default = grey40) |
alpha_selected |
Transparency of selected cluster (default = 1) |
alpha_non_selected |
Transparency of non-selected clusters (default = 0.5) |
colour |
Colour selected = "color_test" or all = "color_all" |
all.colour |
Colours all points by: rainbow, random, heat.colors, terrain.colors, topo.colors, hcl.colors and default |
A ggplot object displaying the network plot.
# Example usage of mcl_cluster function with a stored file example_file <- read.csv(system.file("extdata", "my_data.csv",package = "ClusTCR2")) # Perform clustering using mcl_cluster function step1 <- ClusTCR(example_file,allele = FALSE) # perform mcl step2 <- mcl_cluster(step1) # print the clustering plot after performing step 1 and step 2 print(netplot_ClusTCR2(step2, label = "Name_selected"))# Example usage of mcl_cluster function with a stored file example_file <- read.csv(system.file("extdata", "my_data.csv",package = "ClusTCR2")) # Perform clustering using mcl_cluster function step1 <- ClusTCR(example_file,allele = FALSE) # perform mcl step2 <- mcl_cluster(step1) # print the clustering plot after performing step 1 and step 2 print(netplot_ClusTCR2(step2, label = "Name_selected"))